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Full Description
Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) that teaches agents to learn optimal behavior through interaction, feedback, and long-term goals. After decades of research, RL has matured into a powerful technology driving real-world innovation; it is now used in areas such as robotics, energy systems, finance, and autonomous vehicles.
Yet, for many, RL feels inaccessible, buried under dense mathematics and complex theory. This book changes that. It is designed to help newcomers start applying RL as quickly as possible through a classical pedagogical approach: many small, focused examples that build intuition and practical skill step by step.
Featuring:
• Essential concepts explained from the ground up
• Code-based examples that reveal how algorithms work in practice
• Worked examples by hand to strengthen intuition, just like in engineering or mathematics
• Language-agnostic guidance, easily followed using Python, Java, or C++
Even readers without coding or university-level mathematics backgrounds will gain valuable insight into the fascinating world of RL - insight that may become a critical differentiator in the age of AI. Whether you are a student or professional, Reinforcement Learning Explained will give you the tools and confidence to explore one of AI's most exciting frontiers.
Contents
1 Foreword
2 Scope
3 Reinforcement Learning in a Wider Context
4 Terms, Definitions and Abbreviations
5 Mathematical Foundations
6 Cementing Mathematical Foundations by Hands-on Examples
7 Major Software Components
8 Temporal-Difference Learning
9 Monte Carlo Methods
10 Multi-Step Updating
11 Policy Gradient Methods
12 Actor-Critic Methods
13 Deep Reinforcement Learning
14 Monte Carlo Tree Search
15 Alpha Zero
16 Safe Reinforcement Learning
17 Multi-Agent Reinforcement Learning
18 References
19 Appendix
